MANAGEMENT OF ENDOCRINE DISEASE: Hypoparathyroidism in pregnancy: review and evidence-based recommendations for management
Bibliographic record
Abstract
Purpose Review calcium homeostasis in pregnancy and provide evidence-based best practice recommendations for the management of hypoparathyroidism in pregnancy. Methods We searched MEDLINE, EMBASE and Cochrane databases from January 2000 to April 1, 2018. A total of 65 articles were included in the final review. Conclusions During pregnancy, calcitriol levels increase by two- to-three-fold resulting in enhanced intestinal calcium absorption. The renal filtered calcium load increases leading to hypercalciuria. PTHrP production by the placenta and breasts increases by three-fold, and this may lower the doses of calcium and calcitriol required during pregnancy in mothers with hypoparathyroidism. The literature however describes a wide variation in the required doses of calcium and calcitriol during pregnancy in hypoparathyroid mothers, with some women requiring higher doses of calcitriol, whereas others require lower doses. Close monitoring is necessary as hypercalcemia in the mother may suppress the fetal parathyroid gland development. Also hypocalcemia in the mother is harmful as it may result in secondary hyperparathyroidism in the fetus. This may be associated with demineralization of the fetal skeleton and the development of intrauterine fractures. Inadequate treatment of hypoparathyroidism may also result in uterine contractions and an increased risk of miscarriage. Treatment targets during pregnancy are to maintain a low normal serum calcium. Calcium, calcitriol and vitamin D supplements are safe during pregnancy. Close monitoring of the mother with a multidisciplinary team is advised for optimal care. If calcium homeostasis is well controlled during pregnancy, most women with hypoparathyroidism have an uncomplicated pregnancy and give birth to healthy babies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".